code: def test_correct_results(self, _, im_type, keys, mean, std):
      gaussian_fn = RandGaussianNoised(
          keys=keys, prob=1.0, mean=mean, std=std, dtype=np.float64
      )
      gaussian_fn.set_random_state(seed)
      im = im_type(self.imt)
      noised = gaussian_fn({k: im for k in keys})
      np.random.seed(seed)
      # simulate the randomize() of transform
      np.random.random()
      noise = np.random.normal(
          mean, np.random.uniform(0, std), size=self.imt.shape
      )
      for k in keys:
          expected = self.imt + noise
          if isinstance(noised[k], torch.Tensor):
              noised[k] = noised[k].cpu()
          np.testing.assert_allclose(
              expected, noised[k], atol=1e-5, rtol=1e-5
          )
  
file_path: MONAI/tests/test_rand_gaussian_noised.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.transforms import RandGaussianNoised
  from tests.utils import TEST_NDARRAYS, NumpyImageTestCase2D
label_desc: Use image node_id 1 for calling the TestRandGaussianNoised obj's underlying member method code with example usage: obj.test_correct_results(_, im_type, keys, mean, std) without return types